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[https://csvkit.readthedocs.io/en/latest/ csvkit] is a very efficient tool to manipulate huge amounts of CSV data. Here are some useful tips to manipulate Open Food Facts CSV export.
 
[https://csvkit.readthedocs.io/en/latest/ csvkit] is a very efficient tool to manipulate huge amounts of CSV data. Here are some useful tips to manipulate Open Food Facts CSV export.
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'''Selecting 2 columns'''. Selecting two or three columns can be useful for some usages. Extracting two columns produce a smaller CSV file which can be opened by common softwares such as Libre Office or Excel. The following command creates a CSV file (brands.csv) containing two columns from Open Food Facts (code and brands). (It generally takes more than 2 minutes, depending on your computer.)
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''Selecting 2 column''s. Selecting two or three columns can be useful for some usages. Extracting two columns produce a smaller CSV file which can be opened by common softwares such as Libre Office or Excel. The following command creates a CSV file (brands.csv) containing two columns from Open Food Facts (code and brands). (It generally takes more than 2 minutes, depending on your computer.)
    
<code>
 
<code>
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==== Import CSV in PostGRE SQL ====
 
==== Import CSV in PostGRE SQL ====
 
See this article: https://blog-postgresql.verite.pro/2018/12/21/import-openfoodfacts.html (in french, but should be understandable with Google Translator).
 
See this article: https://blog-postgresql.verite.pro/2018/12/21/import-openfoodfacts.html (in french, but should be understandable with Google Translator).
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==== Python ====
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There are some articles dealing with using Python language to explore Open Food Facts data.
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Step by step commands: http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/notebooks/prepare_data_2017.html (also in french)
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Python notebooks are great to learn Open Food Facts data, as they mix code and results together:
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* Find [https://www.kaggle.com/openfoodfacts/world-food-facts/kernels?sortBy=hotness&group=everyone&pageSize=20&datasetId=20&language=Python dozens of python notebooks on Kaggle]
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* https://www.datasciencesociety.net/part-1-exploring-food-data/
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==== R stat ====
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For people who have R stat skills, there are [https://www.kaggle.com/openfoodfacts/world-food-facts/kernels?sortBy=hotness&group=everyone&pageSize=20&datasetId=20&language=R more than 50 notebooks from Kaggle community].

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